{"id":431,"date":"2026-06-26T11:15:00","date_gmt":"2026-06-26T11:15:00","guid":{"rendered":"https:\/\/www.editage.com\/blog\/?p=431"},"modified":"2026-06-26T05:12:02","modified_gmt":"2026-06-26T05:12:02","slug":"how-to-create-your-scale-for-research-types-of-reliability-validity-in-research","status":"publish","type":"post","link":"https:\/\/www.editage.com\/blog\/how-to-create-your-scale-for-research-types-of-reliability-validity-in-research\/","title":{"rendered":"How to Make a Research Questionnaire\/Scale: Reliability and Validity Analysis"},"content":{"rendered":"\n<p>Contents<\/p>\n\n\n\n<ul><li><a href=\"#_Toc233362763\">What Is a Research Scale?<\/a><\/li><li><a href=\"#_Toc233362764\">Overview of the Scale Development Process<\/a><\/li><li><a href=\"#_Toc233362765\">Types of Reliability in Research<\/a><\/li><li><a href=\"#_Toc233362766\">Summary Comparison of Reliability Types<\/a><\/li><li><a href=\"#_Toc233362767\">Types of Validity in Research<\/a><\/li><li><a href=\"#_Toc233362768\">Summary Comparison of Validity Types<\/a><\/li><li><a href=\"#_Toc233362769\">How Reliability and Validity Interact<\/a><\/li><li><a href=\"#_Toc233362770\">Common Mistakes in Scale Development and How to Avoid Them<\/a><\/li><li><a href=\"#_Toc233362771\">How Should Reliability and Validity Statistics Be Reported in a Research Paper?<\/a><\/li><li><a href=\"#_Toc233362772\">The COSMIN Study Design Checklist<\/a><\/li><li><a href=\"#_Toc233362773\">Frequently Asked Questions<\/a><\/li><\/ul>\n\n\n\n<p>Research scales and <a href=\"https:\/\/www.editage.com\/blog\/questionnaire-survey-research\/\">questionnaires<\/a> are among the most widely used instruments for collecting behavioral, clinical, and psychosocial data. A poorly designed scale can produce inaccurate measurements, introduce systematic bias, and ultimately invalidate study conclusions. Two foundational properties determine whether a scale is fit for purpose: <a href=\"https:\/\/www.editage.com\/blog\/reliability-vs-validity-in-research-types-differences-examples\/\">reliability and validity<\/a>. Reliability concerns consistency, the degree to which a scale produces stable results under comparable conditions. Validity concerns accuracy, the degree to which a scale measures what it claims to measure. Both must be rigorously evaluated before a scale can be trusted in academic or clinical research.<\/p>\n\n\n\n<p>This guide explains every major type of reliability and validity used in scale development, describes how each is calculated, provides benchmark values, and shows how reliability and validity interact. It also covers the step-by-step process for developing a psychometrically sound scale from item generation through final validation.<\/p>\n\n\n\n<h2><a id=\"_Toc233362763\">What Is a Research Scale?<\/a><\/h2>\n\n\n\n<p>A research scale is a structured set of items (questions, statements, or tasks) designed to quantify a latent construct, a concept that cannot be measured directly, such as anxiety, quality of life, patient satisfaction, or health literacy. Scales differ from single-item measures in that they aggregate multiple indicators to produce a composite score, which reduces measurement error and captures the multidimensional nature of most constructs.<\/p>\n\n\n\n<p>Common scale formats include:<\/p>\n\n\n\n<ul><li><a href=\"https:\/\/researcher.life\/blog\/article\/what-is-a-likert-scale-definition-types-and-examples\/\">Likert scales<\/a>: respondents indicate agreement on an ordered response set (e.g., 1 = Strongly Disagree to 5 = Strongly Agree)<\/li><li>Visual analogue scales: respondents mark a position on a continuous line between two anchor points<\/li><li>Semantic differential scales: respondents rate a concept between pairs of bipolar adjectives<\/li><li>Rating scales: respondents assign numerical scores to observable behaviors or performances<\/li><\/ul>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Scale Format<\/strong><\/td><td><strong>Response Type<\/strong><\/td><td><strong>Typical Use<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Likert<\/td><td>Ordered categories (e.g., 1\u20135 or 1\u20137)<\/td><td>Attitudes, perceptions, agreement<\/td><\/tr><tr><td>Visual Analogue<\/td><td>Continuous line marked by respondent<\/td><td>Pain intensity, mood, fatigue<\/td><\/tr><tr><td>Semantic Differential<\/td><td>Bipolar adjective pairs<\/td><td>Brand perception, personality<\/td><\/tr><tr><td>Rating \/ Behaviorally Anchored<\/td><td>Numerical or descriptive anchors<\/td><td>Clinical assessments, performance appraisals<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc233362764\">Overview of the Scale Development Process<\/a><\/h2>\n\n\n\n<p>Developing a valid and reliable scale requires systematic progression through several stages. Skipping or shortchanging any stage weakens psychometric quality.<\/p>\n\n\n\n<ul><li>Stage 1: Define the construct clearly and specify its theoretical boundaries<\/li><li>Stage 2: Generate an initial item pool, typically 3 to 5 times larger than the final intended scale<\/li><li>Stage 3: Conduct expert review for content validity<\/li><li>Stage 4: Conduct cognitive interviews and pilot testing with a small sample<\/li><li>Stage 5: Administer the scale to a representative sample for item analysis<\/li><li>Stage 6: Evaluate reliability (internal consistency, test-retest, inter-rater)<\/li><li>Stage 7: Evaluate validity (content, criterion, construct)<\/li><li>Stage 8: Refine the scale by removing or revising poor-performing items<\/li><li>Stage 9: Cross-validate with an independent sample<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc233362765\">Types of Reliability in Research<\/a><\/h2>\n\n\n\n<p>Reliability is the cornerstone of measurement quality. A scale that is not reliable cannot be valid, because inconsistent results cannot accurately reflect a true underlying construct. Below are the four primary types of reliability evaluated in scale development.<\/p>\n\n\n\n<h3>What Is Internal Consistency Reliability and How Is It Measured?<\/h3>\n\n\n\n<p>Internal consistency reliability reflects the degree to which all items on a scale measure the same underlying construct. High internal consistency indicates that items are interrelated and collectively assess the same latent variable rather than a mix of unrelated concepts.<\/p>\n\n\n\n<p>The most widely used measure is Cronbach&#8217;s alpha (\u03b1), calculated as:<\/p>\n\n\n\n<p>\u03b1 = (k \/ (k \u2212 1)) \u00d7 (1 \u2212 (\u03a3\u03c3\u00b2\u1d62 \/ \u03c3\u00b2\u209c))<\/p>\n\n\n\n<p>where k is the number of items, \u03a3\u03c3\u00b2\u1d62 is the sum of item variances, and \u03c3\u00b2\u209c is the total scale variance.<\/p>\n\n\n\n<p>Interpretation benchmarks:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Cronbach&#8217;s Alpha Value<\/strong><\/td><td><strong>Interpretation<\/strong><\/td><td><strong>Recommended Action<\/strong><\/td><\/tr><\/thead><tbody><tr><td>\u2265 0.90<\/td><td>Excellent<\/td><td>May indicate item redundancy; review for overlap<\/td><\/tr><tr><td>0.80 to 0.89<\/td><td>Good<\/td><td>Suitable for most research contexts<\/td><\/tr><tr><td>0.70 to 0.79<\/td><td>Acceptable<\/td><td>Adequate; consider item revisions<\/td><\/tr><tr><td>0.60 to 0.69<\/td><td>Questionable<\/td><td>Revise or replace poorly performing items<\/td><\/tr><tr><td>&lt; 0.60<\/td><td>Poor \/ Unacceptable<\/td><td>Major item revision or scale redesign required<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>McDonald&#8217;s omega (\u03c9) is an increasingly preferred alternative when the assumption of essentially tau-equivalent measurement is not met, which is common in practice. Omega accounts for varying item factor loadings and provides a more accurate reliability estimate for multidimensional scales.<\/p>\n\n\n\n<p>Item-total correlation is a complementary statistic: items with a corrected item-total correlation below 0.30 are typically candidates for deletion or revision.<\/p>\n\n\n\n<h3>Inter-Rater Reliability<\/h3>\n\n\n\n<p>Inter-rater reliability (IRR) quantifies agreement among two or more independent raters who apply the same scale to the same subjects or observations. It is essential whenever scoring involves subjective judgment, such as behavioral observation, clinical rating, or qualitative coding.<\/p>\n\n\n\n<p>Commonly used IRR statistics:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Statistic<\/strong><\/td><td><strong>Data Type<\/strong><\/td><td><strong>Strengths and Limitations<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Cohen&#8217;s Kappa (\u03ba)<\/td><td>Categorical \/ nominal<\/td><td>Corrects for chance agreement; assumes equal weighting of disagreements<\/td><\/tr><tr><td>Weighted Kappa<\/td><td>Ordinal categories<\/td><td>Accounts for the magnitude of disagreement between ordered categories<\/td><\/tr><tr><td>Intraclass Correlation Coefficient (ICC)<\/td><td>Continuous \/ interval<\/td><td>Preferred for continuous ratings; differentiates absolute agreement from consistency<\/td><\/tr><tr><td>Percentage Agreement<\/td><td>Any<\/td><td>Simple; does not correct for chance; inadequate for formal reporting<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Benchmark values for Kappa and ICC:<\/p>\n\n\n\n<ul><li>&lt; 0.20: Slight agreement<\/li><li>0.21 to 0.40: Fair agreement<\/li><li>0.41 to 0.60: Moderate agreement<\/li><li>0.61 to 0.80: Substantial agreement<\/li><li>&gt; 0.80: Almost perfect agreement<\/li><\/ul>\n\n\n\n<p>For healthcare and clinical decision-making contexts, ICC values of 0.75 or higher are generally required to support instrument use.<\/p>\n\n\n\n<h3>Test-Retest Reliability<\/h3>\n\n\n\n<p>Test-retest reliability evaluates the temporal stability of a scale by administering it to the same participants on two separate occasions and correlating the two sets of scores. A high correlation indicates that the scale produces stable measurements over time, assuming the underlying construct has not genuinely changed between administrations.<\/p>\n\n\n\n<p>Key considerations for test-retest design:<\/p>\n\n\n\n<ul><li>Retest interval: the optimal interval depends on the construct; for stable traits (e.g., personality), two to four weeks is common; for state measures (e.g., mood), shorter intervals of one to two weeks are preferred to minimize true change<\/li><li>Carryover effects: participants may remember prior responses, inflating the correlation; randomizing item order at retest partially mitigates this<\/li><li>Appropriate statistic: Pearson correlation for continuous scores; ICC for repeated measures; Kappa for categorical outputs<\/li><li>Acceptable threshold: correlations of 0.70 or higher are generally considered adequate; 0.80 or higher is preferred for clinical instruments<\/li><\/ul>\n\n\n\n<h3>Split-Half Reliability<\/h3>\n\n\n\n<p>Split-half reliability estimates internal consistency by dividing a scale&#8217;s items into two halves and correlating the summed scores from each half. It is a single-administration method, making it practical when repeated testing is not feasible.<\/p>\n\n\n\n<p>The Spearman-Brown prophecy formula corrects for the reduction in scale length caused by splitting:<\/p>\n\n\n\n<p>r_corrected = (2 \u00d7 r_half-half) \/ (1 + r_half-half)<\/p>\n\n\n\n<p>where r_half-half is the correlation between the two halves.<\/p>\n\n\n\n<p>Common splitting approaches:<\/p>\n\n\n\n<ul><li>Odd-even split: items are assigned to halves based on odd or even item numbers; this approach minimizes positional bias<\/li><li>Random split: items are randomly assigned to each half; results may vary across random splits<\/li><li>Matched split: items are matched on difficulty or content and then distributed to balance the halves<\/li><\/ul>\n\n\n\n<p>Split-half reliability is most informative for scales with a large item pool (generally 20 or more items). For shorter scales, Cronbach&#8217;s alpha is preferred because it is mathematically equivalent to the mean of all possible split-half correlations.<\/p>\n\n\n\n<h3>Parallel Forms Reliability<\/h3>\n\n\n\n<p>Parallel forms reliability (also called alternate forms or equivalent forms reliability) assesses the consistency between two versions of a scale that are designed to measure the same construct with different but equivalent items. This method eliminates recall bias because participants do not see the same items twice.<\/p>\n\n\n\n<p>Requirements for parallel forms:<\/p>\n\n\n\n<ul><li>Both forms must have equal means, variances, and inter-item correlations<\/li><li>Item difficulty and discriminability must be matched across forms<\/li><li>Correlation between forms should be 0.80 or higher to confirm equivalence<\/li><\/ul>\n\n\n\n<p>Parallel forms are most commonly used in educational testing and large-scale epidemiological surveys where practice effects or item exposure must be controlled.<\/p>\n\n\n\n<h2><a id=\"_Toc233362766\">Summary Comparison of Reliability Types<\/a><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Reliability Type<\/strong><\/td><td><strong>What It Measures<\/strong><\/td><td><strong>Key Statistic<\/strong><\/td><td><strong>Minimum Acceptable Value<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Internal Consistency<\/td><td>Item interrelatedness within a single administration<\/td><td>Cronbach&#8217;s alpha; McDonald&#8217;s omega<\/td><td>\u03b1 \u2265 0.70<\/td><\/tr><tr><td>Inter-Rater<\/td><td>Agreement between two or more independent raters<\/td><td>Cohen&#8217;s Kappa; ICC<\/td><td>\u03ba or ICC \u2265 0.70<\/td><\/tr><tr><td>Test-Retest<\/td><td>Temporal stability across two administrations<\/td><td>Pearson r; ICC<\/td><td>r or ICC \u2265 0.70<\/td><\/tr><tr><td>Split-Half<\/td><td>Consistency between two halves of a single administration<\/td><td>Spearman-Brown corrected r<\/td><td>r \u2265 0.70<\/td><\/tr><tr><td>Parallel Forms<\/td><td>Equivalence between two alternate versions of a scale<\/td><td>Pearson r between forms<\/td><td>r \u2265 0.80<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc233362767\">Types of Validity in Research<\/a><\/h2>\n\n\n\n<p>Validity addresses the central question of whether a scale measures what it is intended to measure. A scale can be reliable (consistent) but not valid (inaccurate). Validity is not a single property but a collection of evidence gathered from multiple sources. The major forms of validity relevant to scale development are described below.<\/p>\n\n\n\n<h3>Face Validity<\/h3>\n\n\n\n<p>Face validity is the most basic and subjective form of validity, referring to the surface-level impression that a scale appears to measure its intended construct. It is assessed by having representative members of the target population or lay reviewers examine the items and judge their apparent relevance.<\/p>\n\n\n\n<h4>Characteristics of face validity:<\/h4>\n\n\n\n<ul><li>Not quantifiable in the traditional sense; based on subjective judgment<\/li><li>Important for scale acceptance and participant compliance: respondents who find items irrelevant may disengage or answer carelessly<\/li><li>Does not substitute for rigorous validity evidence; serves as a preliminary screening step<\/li><li>Example: a quality-of-life scale should contain items about physical function, energy, and emotional wellbeing, not medication adherence schedules<\/li><\/ul>\n\n\n\n<h3>Content Validity<\/h3>\n\n\n\n<p>Content validity is established when a scale&#8217;s items comprehensively and proportionally represent all relevant facets of the construct being measured. It ensures that no important domain is omitted and that no irrelevant content is included.<\/p>\n\n\n\n<h4>How Is Content Validity Established?<\/h4>\n\n\n\n<p>Content validity is established primarily through structured expert review. The Content Validity Index (CVI) provides a quantitative measure of expert consensus:<\/p>\n\n\n\n<ul><li>Item-level CVI (I-CVI): the proportion of experts who rate an item as relevant (score 3 or 4 on a 4-point scale); items with I-CVI &lt; 0.78 should be revised or removed<\/li><li>Scale-level CVI (S-CVI\/Ave): the average I-CVI across all items; values of 0.90 or higher indicate acceptable content validity<\/li><li>S-CVI\/Universal Agreement: the proportion of items rated as relevant by all experts; a value of 0.80 or higher is recommended<\/li><\/ul>\n\n\n\n<h4>Steps to establish content validity:<\/h4>\n\n\n\n<ul><li>Step 1: Identify a panel of subject-matter experts (typically 5 to 10 experts)<\/li><li>Step 2: Ask experts to rate each item on a 4-point scale (1 = not relevant to 4 = highly relevant)<\/li><li>Step 3: Calculate I-CVI for each item and S-CVI for the overall scale<\/li><li>Step 4: Revise or remove items with insufficient I-CVI values<\/li><li>Step 5: Repeat until adequate S-CVI is achieved<\/li><\/ul>\n\n\n\n<h3>Criterion Validity<\/h3>\n\n\n\n<p>Criterion validity evaluates how well scores on a scale predict or relate to an external criterion, a gold-standard measure or real-world outcome. It is the empirical backbone of validity evidence and is subdivided into concurrent validity and predictive validity based on the temporal relationship between the scale and the criterion.<\/p>\n\n\n\n<h4>Types of criterion validity:<\/h4>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Subtype<\/strong><\/td><td><strong>Criterion Timing<\/strong><\/td><td><strong>Example<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Concurrent Validity<\/td><td>Criterion measured at the same time as the scale<\/td><td>Comparing a new depression scale to the Hamilton Depression Rating Scale administered simultaneously<\/td><\/tr><tr><td>Predictive Validity<\/td><td>Criterion measured in the future<\/td><td>Using a health literacy scale to predict medication adherence outcomes six months later<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4>Statistical methods for criterion validity:<\/h4>\n\n\n\n<ul><li><a href=\"https:\/\/www.editage.com\/blog\/pearson-correlation-coefficient-definition-examples\/\">Pearson correlation coefficient<\/a>: for two continuous measures<\/li><li>Spearman correlation: for ordinal or non-normally distributed scores<\/li><li>Receiver operating characteristic (ROC) analysis: to assess a scale&#8217;s ability to discriminate between known groups and to identify optimal cut-off scores<\/li><li><a href=\"https:\/\/www.editage.com\/blog\/what-is-regression-and-types-of-regression-for-biomedical-researchers\/\">Regression analysis:<\/a> to quantify the variance in the criterion explained by scale scores<\/li><\/ul>\n\n\n\n<p>Correlations of 0.50 or higher between a new scale and a validated criterion measure are generally considered supportive of criterion validity, though field-specific norms apply.<\/p>\n\n\n\n<h3>Construct Validity: Convergent and Divergent Evidence<\/h3>\n\n\n\n<p>Construct validity is the most comprehensive form of validity evidence and refers to the degree to which a scale accurately represents the theoretical construct it is designed to measure. It encompasses all evidence that bears on the interpretation of scale scores, including convergent validity and divergent (discriminant) validity.<\/p>\n\n\n\n<h3>Convergent Validity<\/h3>\n\n\n\n<p>Convergent validity exists when scores on a new scale correlate positively and substantially with scores on other established scales that measure the same or highly related constructs. Strong convergent validity supports the interpretation that the new scale captures the intended construct.<\/p>\n\n\n\n<p>Example: a newly developed anxiety scale should produce high positive correlations with the Generalized Anxiety Disorder scale (GAD-7) and the State-Trait Anxiety Inventory (STAI), because both assess anxiety. Correlations of 0.50 or higher are typically expected.<\/p>\n\n\n\n<h3>Divergent (Discriminant) Validity<\/h3>\n\n\n\n<p>Divergent validity, also called discriminant validity, exists when scores on a new scale show low or non-significant correlations with scales measuring theoretically unrelated constructs. It confirms that the scale is capturing something distinct rather than a general factor such as social desirability or negative affectivity.<\/p>\n\n\n\n<p>Example: a fatigue scale should not correlate highly with a measure of racial prejudice, because these are conceptually unrelated constructs. A correlation below 0.30 with an unrelated measure is generally considered acceptable evidence of discriminant validity.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Validity Type<\/strong><\/td><td><strong>Expected Pattern<\/strong><\/td><td><strong>Typical Correlation Threshold<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Convergent<\/td><td>High correlation with related measures<\/td><td>r \u2265 0.50<\/td><\/tr><tr><td>Divergent \/ Discriminant<\/td><td>Low correlation with unrelated measures<\/td><td>r \u2264 0.30<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3>Concurrent Validity<\/h3>\n\n\n\n<p>Concurrent validity is the subtype of criterion validity in which a new scale is compared to a criterion measure administered at the same time. It is commonly used when a new scale is proposed as a shorter, more feasible alternative to an existing gold-standard instrument.<\/p>\n\n\n\n<p>Example: if researchers develop a brief 10-item balance assessment tool, they may administer it alongside the Timed Up and Go test and the Berg Balance Scale during the same clinical visit. A high correlation between the new tool and these established measures, typically r \u2265 0.70, constitutes evidence of concurrent validity.<\/p>\n\n\n\n<p>Concurrent validity is especially important in clinical settings where the criterion measure is too time-consuming, expensive, or burdensome for routine use.<\/p>\n\n\n\n<h3>Known-Groups Validity<\/h3>\n\n\n\n<p>Known-groups validity (also called contrasted-groups validity) demonstrates that a scale can differentiate between groups that are theoretically expected to differ on the measured construct. It is particularly useful when no single gold-standard criterion exists.<\/p>\n\n\n\n<p>Examples:<\/p>\n\n\n\n<ul><li>A depression scale should produce significantly higher scores in a sample diagnosed with major depressive disorder than in a healthy <a href=\"https:\/\/www.editage.com\/blog\/control-group\/\">control group<\/a><\/li><li>A health literacy scale should produce higher scores among healthcare professionals than among the general population<\/li><\/ul>\n\n\n\n<p>Statistical methods include <a href=\"https:\/\/www.editage.com\/blog\/t-test-definition-assumptions-formula-calculation\/\">independent-samples t-tests<\/a>, Mann-Whitney U tests (for non-normal distributions), and <a href=\"https:\/\/www.editage.com\/blog\/effect-size\/\">effect size calculations<\/a> (Cohen&#8217;s d). A large effect size (d \u2265 0.80) provides compelling known-groups evidence.<\/p>\n\n\n\n<h3>Structural Validity: Exploratory and Confirmatory Factor Analysis<\/h3>\n\n\n\n<p>Structural validity examines whether the internal structure of a scale, the pattern of relationships among items, matches the theoretical structure of the construct. It is assessed through factor analysis.<\/p>\n\n\n\n<h4>Exploratory Factor Analysis (EFA):<\/h4>\n\n\n\n<ul><li>Used in early-stage scale development when the factor structure is unknown or hypothetical<\/li><li>Identifies the number of underlying factors and the pattern of item loadings<\/li><li>Items should load at 0.40 or higher on their primary factor and below 0.30 on non-target factors<\/li><li>Common extraction methods: principal axis factoring; common factor analysis<\/li><li>Common rotation methods: varimax (orthogonal, for independent factors); oblimin or promax (oblique, for correlated factors)<\/li><\/ul>\n\n\n\n<h4>Confirmatory Factor Analysis (CFA):<\/h4>\n\n\n\n<ul><li>Used to test a pre-specified factor structure with a new sample<\/li><li>Evaluates model fit using multiple indices<\/li><\/ul>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Fit Index<\/strong><\/td><td><strong>Acceptable Range<\/strong><\/td><td><strong>Good Fit Threshold<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Chi-square \/ df ratio (\u03c7\u00b2\/df)<\/td><td>\u2264 5.0<\/td><td>\u2264 3.0<\/td><\/tr><tr><td>Comparative Fit Index (CFI)<\/td><td>\u2265 0.90<\/td><td>\u2265 0.95<\/td><\/tr><tr><td>Tucker-Lewis Index (TLI)<\/td><td>\u2265 0.90<\/td><td>\u2265 0.95<\/td><\/tr><tr><td>RMSEA (Root Mean Square Error of Approximation)<\/td><td>\u2264 0.08<\/td><td>\u2264 0.06<\/td><\/tr><tr><td>SRMR (Standardized Root Mean Square Residual)<\/td><td>\u2264 0.10<\/td><td>\u2264 0.08<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc233362768\">Summary Comparison of Validity Types<\/a><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Validity Type<\/strong><\/td><td><strong>Core Question<\/strong><\/td><td><strong>Primary Method<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Face<\/td><td>Do items look relevant to respondents?<\/td><td>Stakeholder review; lay panel<\/td><\/tr><tr><td>Content<\/td><td>Do items cover all relevant domains?<\/td><td>Expert panel; Content Validity Index (CVI)<\/td><\/tr><tr><td>Criterion (Concurrent)<\/td><td>Do scores match a criterion measured simultaneously?<\/td><td>Correlation; ROC analysis<\/td><\/tr><tr><td>Criterion (Predictive)<\/td><td>Do scores predict future outcomes?<\/td><td>Regression; longitudinal follow-up<\/td><\/tr><tr><td>Convergent<\/td><td>Do scores correlate with related scales?<\/td><td>Correlation matrix; MTMM<\/td><\/tr><tr><td>Divergent \/ Discriminant<\/td><td>Are scores distinct from unrelated constructs?<\/td><td>Correlation matrix; AVE vs. shared variance<\/td><\/tr><tr><td>Known-Groups<\/td><td>Can the scale distinguish expected group differences?<\/td><td>Group comparison; effect size (Cohen&#8217;s d)<\/td><\/tr><tr><td>Structural<\/td><td>Does the factor structure match theory?<\/td><td>EFA; CFA; fit indices<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc233362769\">How Reliability and Validity Interact<\/a><\/h2>\n\n\n\n<p>Reliability and validity are related but distinct. A reliable scale is a prerequisite for validity, but reliability alone does not guarantee validity. The relationship can be summarized as follows:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Scenario<\/strong><\/td><td><strong>Reliable?<\/strong><\/td><td><strong>Valid?<\/strong><\/td><td><strong>Implication<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Consistent results measuring the wrong construct<\/td><td>Yes<\/td><td>No<\/td><td>Scale is precise but inaccurate; construct definition may be flawed<\/td><\/tr><tr><td>Inconsistent results measuring the right construct<\/td><td>No<\/td><td>No<\/td><td>Scale is neither precise nor accurate; fundamental revision required<\/td><\/tr><tr><td>Consistent results measuring the right construct<\/td><td>Yes<\/td><td>Yes<\/td><td>Ideal; scale is psychometrically sound<\/td><\/tr><tr><td>Inconsistent results that happen to average to the right value<\/td><td>No<\/td><td>Possibly, by chance only<\/td><td>Not scientifically acceptable<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3>Practical implications:<\/h3>\n\n\n\n<ul><li>Improving internal consistency does not automatically improve validity; a scale with 20 redundant items may have very high alpha but poor content coverage<\/li><li>Increasing scale length generally increases reliability but may reduce face validity and participant compliance<\/li><li>Construct validity subsumes all forms of validity evidence; it is the overarching standard for evaluating a scale&#8217;s interpretive meaning<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc233362770\">Common Mistakes in Scale Development and How to Avoid Them<\/a><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td><strong>Mistake<\/strong><\/td><td><strong>Consequence<\/strong><\/td><td><strong>Prevention Strategy<\/strong><\/td><\/tr><\/thead><tbody><tr><td>Defining the construct too broadly<\/td><td>Items measure multiple unrelated things; low validity<\/td><td>Conduct a thorough <a href=\"https:\/\/www.editage.com\/blog\/what-is-literature-review-definition-types-and-examples\/\">literature review<\/a>; specify construct boundaries before item writing<\/td><\/tr><tr><td>Generating too few items initially<\/td><td>Insufficient items survive refinement; underpowered scale<\/td><td>Generate 3 to 5 times the intended final number of items<\/td><\/tr><tr><td>Neglecting reverse-scored items<\/td><td>Acquiescence bias inflates reliability estimates<\/td><td>Include 20-40 percent negatively worded or reverse-scored items<\/td><\/tr><tr><td>Using a convenience sample for validation<\/td><td>Validity evidence may not generalize<\/td><td>Use a diverse, representative sample that matches the intended population<\/td><\/tr><tr><td>Relying on alpha alone for reliability<\/td><td>Alpha underestimates reliability for non-unidimensional scales<\/td><td>Report McDonald&#8217;s omega and item-total correlations alongside alpha<\/td><\/tr><tr><td>Treating face validity as sufficient<\/td><td>Scale may appear valid but fail empirical tests<\/td><td>Always supplement face validity with content validity indices and criterion validity<\/td><\/tr><tr><td>Omitting cross-validation<\/td><td>Over-fitted factor structure fails to replicate<\/td><td>Split sample into development and validation subsamples or use an independent replication sample<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2><a id=\"_Toc233362771\">How Should Reliability and Validity Statistics Be Reported in a Research Paper?<\/a><\/h2>\n\n\n\n<p>Complete and transparent reporting of psychometric properties allows readers and reviewers to evaluate the quality of the measurement instrument. The following information should be reported for each scale used or developed in a study.<\/p>\n\n\n\n<h3>Reliability reporting checklist:<\/h3>\n\n\n\n<ul><li>Cronbach&#8217;s alpha (and McDonald&#8217;s omega if available) with confidence intervals<\/li><li>Inter-rater reliability statistic (Kappa or ICC), number of raters, and rater training procedures<\/li><li>Test-retest correlation and the retest interval used<\/li><li>Item-total correlations for each item<\/li><\/ul>\n\n\n\n<h3>Validity reporting checklist:<\/h3>\n\n\n\n<ul><li>Content Validity Index values (I-CVI per item; S-CVI for the scale)<\/li><li>Correlation coefficients for convergent and divergent validity with confidence intervals<\/li><li>Factor analysis results: factor loadings, eigenvalues, variance explained, fit indices for CFA<\/li><li>ROC curve statistics if cut-off scores are being established: area under the curve (AUC), sensitivity, specificity<\/li><li>Known-groups comparison results: group means, standard deviations, test statistic, and effect size<\/li><\/ul>\n\n\n\n<h2><a id=\"_Toc233362772\">The COSMIN Study Design Checklist<\/a><\/h2>\n\n\n\n<p>The COSMIN Study Design Checklist is directly relevant to any researcher developing or validating a scale. COSMIN stands for COnsensus-based Standards for the selection of health Measurement Instruments, and the checklist was developed through an international Delphi study as a tool for evaluating the methodological quality of studies on measurement properties. For scale developers, it functions both as a planning framework before data collection and as a reporting standard for manuscripts. <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC2852520\/\" target=\"_blank\" rel=\"noreferrer noopener\">&nbsp;<\/a><\/p>\n\n\n\n<p>The checklist can be used when selecting a measurement instrument, peer-reviewing a manuscript, designing or reporting a study on measurement properties, or for educational purposes.<\/p>\n\n\n\n<h3>What Does the COSMIN Study Design Checklist Cover?<\/h3>\n\n\n\n<p>The COSMIN Study Design Checklist consists of ten boxes. The first box contains general recommendations for designing a study on measurement properties and is relevant for all studies. The remaining boxes contain standards for specific studies on each of the nine measurement properties: content validity, structural validity, internal consistency, cross-cultural validity and measurement invariance, reliability, measurement error, criterion validity, hypotheses testing for construct validity, and responsiveness.<\/p>\n\n\n\n<h3>The Three Quality Domains<\/h3>\n\n\n\n<p>In assessing the quality of a health measurement instrument, COSMIN distinguishes three quality domains: reliability, validity, and responsiveness. The domain of reliability contains three measurement properties: internal consistency, reliability, and measurement error. The domain of validity also contains three measurement properties: content validity, construct validity, and criterion validity. The domain of responsiveness contains only one measurement property, also called responsiveness. <a href=\"https:\/\/faculty.ksu.edu.sa\/sites\/default\/files\/cosmin_checklist_manual_v9.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">&nbsp;<\/a><\/p>\n\n\n\n<h3>The Ten Boxes at a Glance<\/h3>\n\n\n\n<ul><li><strong>Box 1, General Design Requirements:<\/strong> applies to every study; covers clarity of research aim, description of the instrument, and description of the target population<\/li><li><strong>Box 2, Content Validity:<\/strong> standards for evaluating the relevance, comprehensiveness, and comprehensibility of items<\/li><li><strong>Box 3, Structural Validity:<\/strong> standards for factor analysis studies evaluating the internal structure of a scale<\/li><li><strong>Box 4, Internal Consistency:<\/strong> standards for studies using Cronbach&#8217;s alpha, McDonald&#8217;s omega, or item-total correlations<\/li><li><strong>Box 5, Cross-Cultural Validity and Measurement Invariance:<\/strong> standards for studies comparing instrument behavior across language, ethnicity, gender, or disease subgroups<\/li><li><strong>Box 6, Reliability:<\/strong> standards for test-retest, inter-rater, and intra-rater reliability studies<\/li><li><strong>Box 7, Measurement Error:<\/strong> standards for calculating the Standard Error of Measurement (SEM) and the Smallest Detectable Change (SDC)<\/li><li><strong>Box 8, Criterion Validity:<\/strong> standards for comparing a new instrument to a gold-standard criterion<\/li><li><strong>Box 9, Hypotheses Testing for Construct Validity:<\/strong> standards for convergent and divergent validity studies, including specification of a priori hypotheses<\/li><li><strong>Box 10, Responsiveness:<\/strong> standards for studies evaluating whether a scale can detect meaningful change over time<\/li><\/ul>\n\n\n\n<h3>How the Rating System Works<\/h3>\n\n\n\n<p>Each standard within a box is rated on a 4-point scale. The rating labels are: very good, adequate, doubtful, and inadequate. A total rating per box is obtained by taking the lowest rating of any single item in the box, the so-called worst score counts principle. This method was chosen because poor methodological aspects of a study cannot be compensated by good aspects.<\/p>\n\n\n\n<h3>The Four-Step Procedure for Applying the Checklist<\/h3>\n\n\n\n<p>To complete the COSMIN checklist, a four-step procedure should be followed.<\/p>\n\n\n\n<ol type=\"1\"><li>Step 1 is to determine which properties are evaluated in an article.<\/li><li>Step 2 is to determine if the statistical methods used in the article are based on Classical Test Theory (CTT) or on Item Response Theory (IRT); for studies that apply IRT, the IRT box should be completed.<\/li><li>Step 3 is to complete the boxes with standards accompanying the properties chosen in Step 1.<\/li><li>Step 4 is to complete the box on general requirements for the generalizability of the results.<\/li><\/ol>\n\n\n\n<h3>The Modular Design Principle<\/h3>\n\n\n\n<p>The COSMIN checklist should be used as a modular tool. This means that it may not be necessary to complete the whole checklist when evaluating the quality of a particular study. The measurement properties evaluated in the study determine which boxes are relevant. For example, if in a study the internal consistency and reliability of an instrument were assessed, only those two boxes need to be completed. If measurement error was not assessed, that box does not need to be completed. <a href=\"https:\/\/faculty.ksu.edu.sa\/sites\/default\/files\/cosmin_checklist_manual_v9.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">&nbsp;<\/a><\/p>\n\n\n\n<h3>When Should Researchers Use the COSMIN Study Design Checklist?<\/h3>\n\n\n\n<ul><li><strong>Before data collection:<\/strong> use it to audit the planned study design against each relevant box, ensuring sample characteristics, retest intervals, rater procedures, and statistical methods are pre-specified correctly<\/li><li><strong>During item development:<\/strong> use the content validity box to confirm that expert review and patient input procedures meet the required standards<\/li><li><strong>Before submission:<\/strong> use it as a self-audit to check that all relevant measurement properties are reported with sufficient methodological detail<\/li><li><strong>For <a href=\"https:\/\/www.editage.com\/blog\/conducting-and-reporting-systematic-reviews\/\">systematic reviews<\/a>:<\/strong> use it to rate the risk of bias in published studies on existing instruments before synthesizing evidence across studies<\/li><li><strong>For peer review:<\/strong> use it to identify specific methodological gaps in manuscripts reporting scale development or validation<\/li><\/ul>\n\n\n\n<h3>A Notable Requirement: Patient Involvement in Content Validity<\/h3>\n\n\n\n<p>One of the most consequential updates in the COSMIN framework concerns content validity. Content validity is considered the most important measurement property of a patient-reported outcome measure and the most challenging to assess. The updated COSMIN standards require that content validity studies include not only expert review but also direct input from members of the target patient population, assessing relevance, comprehensiveness, and comprehensibility. Scales developed without patient involvement in item generation are rated as having inadequate content validity under the COSMIN framework, regardless of their expert CVI scores.<\/p>\n\n\n\n<h2><a id=\"_Toc233362773\">Frequently Asked Questions<\/a><\/h2>\n\n\n\n<h3>What is the difference between reliability and validity in research?<\/h3>\n\n\n\n<p>Reliability refers to the consistency of a scale: it produces stable, reproducible results under the same conditions. Validity refers to accuracy: the scale genuinely measures the construct it claims to measure. A reliable scale is not necessarily valid, but a valid scale must be reliable. For example, a bathroom scale that consistently reads 5 pounds too high is reliable but not valid.<\/p>\n\n\n\n<h3>What is a good Cronbach&#8217;s alpha value for a research scale?<\/h3>\n\n\n\n<p>A Cronbach&#8217;s alpha of 0.70 or higher is the minimum threshold widely accepted for research purposes. Values between 0.80 and 0.90 indicate good reliability. Values above 0.90 may signal item redundancy, where multiple items are essentially asking the same question. The acceptable threshold can be higher (0.80 or more) for scales used in clinical decision-making.<\/p>\n\n\n\n<h3>How many experts are needed to establish content validity?<\/h3>\n\n\n\n<p>A panel of 5 to 10 subject-matter experts is the most commonly recommended size for content validity review. Panels smaller than 5 may produce unstable Content Validity Index values due to the outsized impact of any single expert&#8217;s rating. Some sources recommend larger panels (10 to 15) for multidimensional scales with many domains to cover.<\/p>\n\n\n\n<h3>Can a scale have high internal consistency but low validity?<\/h3>\n\n\n\n<p>Yes. A scale can be highly internally consistent, meaning all items correlate with each other, while still measuring the wrong construct or a narrow slice of a broader construct. For example, 20 items all asking about fear of public speaking may produce a high alpha, but if the intended construct is general anxiety, the scale lacks construct validity because it omits panic, worry, and social avoidance domains.<\/p>\n\n\n\n<h3>What is the difference between convergent validity and concurrent validity?<\/h3>\n\n\n\n<p>Convergent validity is a form of construct validity: it examines whether a scale correlates with other measures of theoretically related constructs, regardless of when those measures are administered. Concurrent validity is a form of criterion validity: it examines whether a scale correlates with a specific criterion measure administered at the same point in time. The key difference is whether the focus is on conceptual relationships (convergent) or predictive accuracy against a criterion (concurrent).<\/p>\n\n\n\n<h3>When should I use confirmatory factor analysis instead of exploratory factor analysis?<\/h3>\n\n\n\n<p>Exploratory factor analysis (EFA) is appropriate when the underlying factor structure is unknown or only loosely theorized, typically in early-stage scale development. Confirmatory factor analysis (CFA) is appropriate when a specific factor structure has been proposed based on theory or prior EFA and a new, independent sample is available to test whether the data fit that model. Best practice is to conduct EFA on one subsample and CFA on a separate validation subsample.<\/p>\n\n\n\n<h3>How is inter-rater reliability improved in practice?<\/h3>\n\n\n\n<p>Inter-rater reliability can be improved through several strategies: detailed operational definitions for each rating category, standardized rater training using calibration exercises, the use of anchor examples or benchmark cases, regular reliability checks during data collection, and rater drift monitoring across the study period. When possible, consensus rating procedures (where raters discuss and resolve disagreements) should be pre-specified in the study protocol.<\/p>\n\n\n\n<h3>Is it necessary to report all types of reliability and validity in a single study?<\/h3>\n\n\n\n<p>Not always. The types of reliability and validity that must be reported depend on the research context, the intended use of the scale, and the target journal. At minimum, internal consistency and at least one form of validity evidence are expected. For clinical measurement instruments, test-retest reliability and criterion validity are also typically required. Journal reporting guidelines such as the COSMIN checklist provide specific requirements for health measurement instruments.<\/p>\n","protected":false},"excerpt":{"rendered":"In healthcare research, scales and questionnaires are super common tools used to collect data about behavior. But did you know that poorly designed scales can lead to unreliable data and a waste of resources? \n\nTo make sure your scale is top-notch, you need to focus on two key aspects of quality: reliability and validity. These are kind of complicated concepts, so let's break them down a bit. Let's take a look at the different types of reliability and validity that you need to calculate to ensure the scale or measure you're using is psychometrically sound.","protected":false},"author":2,"featured_media":433,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_ayudawp_aiss_exclude":false,"_ayudawp_aiss_summary":"It is the empirical backbone of validity evidence and is subdivided into concurrent validity and predictive validity based on the temporal relationship between the scale and the criterion. The remaining boxes contain standards for specific studies on each of the nine measurement properties: content validity, structural validity, internal consistency, cross-cultural validity and measurement invariance, reliability, measurement error, criterion validity, hypotheses testing for construct validity, and responsiveness. The domain of validity also contains three measurement properties: content validity, construct validity, and criterion validity.","_ayudawp_aiss_summary_provider":"extractive","_ayudawp_aiss_summary_hash":"842ee4a10b9ced5f91627448ae4b93d7b816fedf"},"categories":[14],"tags":[23,24],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to Make a Research Questionnaire\/Scale: Reliability and Validity Analysis | Editage<\/title>\n<meta name=\"description\" content=\"This article explains every major type of reliability and validity used in scale development, how each is calculated, benchmark values, and how reliability and validity interact.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.editage.com\/blog\/how-to-create-your-scale-for-research-types-of-reliability-validity-in-research\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Make a Research Questionnaire\/Scale: Reliability and Validity Analysis | Editage\" \/>\n<meta property=\"og:description\" content=\"This article explains every major type of reliability and validity used in scale development, how each is calculated, benchmark values, and how reliability and validity interact.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.editage.com\/blog\/how-to-create-your-scale-for-research-types-of-reliability-validity-in-research\/\" \/>\n<meta property=\"og:site_name\" content=\"Educational Articles For Researchers, Students And Authors - 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